{"as_of":"2026-08-07T18:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d2a7ee42089df593eb3128d8e7f8c4204a44b36247dceceaac6a86dc288fb586","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:56:50.035366Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.23042/citation-record","integrity":"/paper/2506.23042/integrity","json":"/paper/2506.23042/citation-record.json","paper":"/paper/2506.23042"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:53.627000Z","title":"3d gaussian splatting for real-time radiance field rendering,","venue":null,"work_id":"a049a722-8d8f-491d-95f9-b44aa54bee5a","year":2023},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:47.405892Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:b7835b127270a235bc39628fd38084bde1c3b29e588ceefff7401fef653467f2","observation_id":"030ee12b-ca0f-4fdd-b016-5e4425642257","resolution":{"observed_at":"2026-08-06T21:56:53.680735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2503.14475","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:50.991643Z","title":"Optimized 3d gaussian splatting using coarse-to-fine image frequency modulation,","venue":null,"work_id":"8c4bc6e4-7016-4ca7-a15c-360bfbda0243","year":2025},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:47.445901Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:9dbf2069d9fd88120d27f1b2720b8f28b03fd903e9191373e2e04a6187f7e4fa","observation_id":"e66e0052-6ea4-4c36-a88f-58745f9a8973","resolution":{"observed_at":"2026-08-06T21:56:51.048892Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:53.394958Z","title":"Radiative gaussian splatting for efficient x-ray novel view synthesis,","venue":null,"work_id":"ef079be2-263b-4a4b-8189-5f5a287461d8","year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:47.509555Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:163c6ee3682bd17412fcb374e7133f766127b771ac72b73e59fa4b9e53f84db0","observation_id":"500a6f29-c2d0-42f4-8fa8-37e15a47a3c5","resolution":{"observed_at":"2026-08-06T21:56:53.483858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:53.128197Z","title":"R2- gaussian: Rectifying radiative gaussian splatting for tomo- graphic reconstruction,","venue":null,"work_id":"1b232fa0-581a-4caa-913e-9811490ad13b","year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:47.569869Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:b2a494f94ccdcbe6a12cd88b3688351b0afae5660fdf128f89fa1b4a7da5cb2c","observation_id":"419c733d-d145-4a92-9000-983c54415154","resolution":{"observed_at":"2026-08-06T21:56:53.265915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:52.995394Z","title":"Gs-slam: Dense visual slam with 3d gaussian splatting,","venue":null,"work_id":"dc72ab60-723c-49dc-afc8-e1dfff76e230","year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:47.658139Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:39fe0db90f8d4c5658b60b613e835499dfaed137490b0617446af8e84d984e83","observation_id":"3cf4720c-f723-4bb9-8a34-d4afa9dd39ad","resolution":{"observed_at":"2026-08-06T21:56:53.034493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.11979","last_updated":"2025-03-15T03:20:14Z","snapshot_observed_at":"2026-08-07T17:02:32.672491Z","submitted_at":"2025-03-15T03:20:14Z","title":"DynaGSLAM: Real-Time Gaussian-Splatting SLAM for Online Rendering, Tracking, Motion Predictions of Moving Objects in Dynamic Scenes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.11979","snapshot_observed_at":"2026-08-06T21:56:47.718021Z","title":"Dynagslam: Real-time gaussian-splatting slam for online rendering, tracking, motion predictions of moving objects in dynamic scenes,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:47.718021Z"},"links":{"cited_paper":"/paper/2503.11979","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:a6e9559ac43ecfe6f5e343384bb71b8884148dc1423ab6e997c530aa76718f8f","observation_id":"4d559f58-3cdf-455a-a61b-84d91ad0660e","resolution":{"observed_at":"2026-08-06T21:56:47.718021Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.10144","last_updated":"2025-05-15T10:17:48Z","snapshot_observed_at":"2026-08-07T15:45:08.002021Z","submitted_at":"2025-05-15T10:17:48Z","title":"VRSplat: Fast and Robust Gaussian Splatting for Virtual Reality","version":1},"cited_work":{"arxiv_id":"2505.10144","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.10144","snapshot_observed_at":"2026-08-06T21:56:50.820344Z","title":"VRSplat: Fast and Robust Gaussian Splatting for Virtual Reality","venue":"cs.GR","work_id":"28a253e0-c312-4db3-9a62-d0019f185160","year":2025},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:47.783334Z"},"links":{"cited_paper":"/paper/2505.10144","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:5a2ea319c1a77e1ae53582c76607cd8134b5bc263996ce30cda96b591d89d68d","observation_id":"14e250e7-1b5d-43ce-93a8-30eee9e79663","resolution":{"observed_at":"2026-08-06T21:56:50.870683Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:52.824567Z","title":"Vr-splatting: Foveated radiance field rendering via 3d gaussian splatting and neural points,","venue":null,"work_id":"1722c52d-c56e-490d-9e1e-84f93eb2089f","year":2025},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:47.839254Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:b706d2f0e46ea14d74d4d1dbbded6d6f78df6381754006ea2427f3eb37e082f1","observation_id":"ca5341f5-dca7-4fc7-84bd-e5935fea966e","resolution":{"observed_at":"2026-08-06T21:56:52.925310Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:47.898509Z","title":"Splatsdf: Boosting neural implicit sdf via gaussian splatting fusion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:47.898509Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:33fdb867c16a509e7f9f9bd53e9000de799451e8ef0b03347e93e7d013094b61","observation_id":"c5f73a5e-7b11-4d64-a8c3-07a60adf96f0","resolution":{"observed_at":"2026-08-06T21:56:47.898509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:52.783707Z","title":"Mono- selfrecon: Purely self-supervised explicit generalizable 3d reconstruction of indoor scenes from monocular rgb views,","venue":null,"work_id":"f5d264a0-236c-43f3-a390-5f3894b5c12b","year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:47.991195Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:854dd9c776ae6981868b78d9799b176030ffce3f42a280f2e9ef99091355dd53","observation_id":"7418cddc-aa64-4cd4-9384-29b0fbf77356","resolution":{"observed_at":"2026-08-06T21:56:52.811541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:52.607518Z","title":"S3gaussian: Self-supervised street gaussians for autonomous driving,","venue":null,"work_id":"7eb6eb3c-7eb5-4122-8c46-3622710ff6fa","year":null},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.053938Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:04c44923bac661188a3251e1936047b2a382bf43c70079558a947bb3ba6c3107","observation_id":"d02702e9-7f86-42c3-b687-facf43418f83","resolution":{"observed_at":"2026-08-06T21:56:52.696525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:48.161496Z","title":"Rad: Training an end-to-end driving policy via large-scale 3dgs-based reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.161496Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:1713a10fb6629fa40727f489746f78a2e98476ea91705a0e50d1a801eeb7f626","observation_id":"923dcc15-111f-40ad-a23c-730aa9c13307","resolution":{"observed_at":"2026-08-06T21:56:48.161496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:52.438268Z","title":"Nerf: Representing scenes as neural radiance fields for view synthesis,","venue":null,"work_id":"d47569a8-b093-4adf-b382-b2cea7ca7e21","year":2020},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.191391Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:c23fce7825738a5ce98a0ff0016b2b09f1606e8edb902ff07b8e31b19680432d","observation_id":"3f2a51c8-5c37-4659-8c96-ad3e935cb6e5","resolution":{"observed_at":"2026-08-06T21:56:52.495722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:52.364765Z","title":"Com- pressed 3d gaussian splatting for accelerated novel view syn- thesis,","venue":null,"work_id":"a633fb18-012d-42fa-9fe1-9aff7788174d","year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.255809Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:1513e29710404b5ff21cfe5f24a755f80dd3a3c13d37d591cc3cb83482a2bf7a","observation_id":"2942a205-36f2-47d2-b8c0-64f6bab97155","resolution":{"observed_at":"2026-08-06T21:56:52.405358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.16084","last_updated":"2024-03-31T04:45:58Z","snapshot_observed_at":"2026-07-06T17:08:08.775293Z","submitted_at":"2023-12-26T15:14:37Z","title":"LangSplat: 3D Language Gaussian Splatting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.16084","snapshot_observed_at":"2026-08-06T21:56:48.399085Z","title":"Langsplat: 3d language gaussian splatting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.399085Z"},"links":{"cited_paper":"/paper/2312.16084","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:1346c06ef78ef0de043fe16cd7f0d38b9ee41b191834a853f44299db5592501d","observation_id":"0f81ee24-8e2d-4e8d-a332-4becc54a7b7e","resolution":{"observed_at":"2026-08-06T21:56:48.399085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.18482","last_updated":"2023-11-30T11:50:07Z","snapshot_observed_at":"2026-08-04T01:46:39.746604Z","submitted_at":"2023-11-30T11:50:07Z","title":"Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.18482","snapshot_observed_at":"2026-08-06T21:56:48.448368Z","title":"Language embedded 3d gaussians for open-vocabulary scene under- standing,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.448368Z"},"links":{"cited_paper":"/paper/2311.18482","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:82f8060a81dfd387dd3c95b84672ab2e7206c0db6e459117613bc9b3e758c4c1","observation_id":"04342b13-ad89-486a-a112-2310bfd4647a","resolution":{"observed_at":"2026-08-06T21:56:48.448368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:52.275119Z","title":"WildGaussians: 3D gaussian splatting in the wild,","venue":null,"work_id":"20dc6aa2-c8df-496c-8799-7de7f013b7df","year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.504043Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:fa34df1151c01b4bc2b3917ba639aa34806bd87b156ea2e53b8d91e03ff063d7","observation_id":"ef6e786c-c0c8-4e7b-b189-18a2ac1f8983","resolution":{"observed_at":"2026-08-06T21:56:52.308463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:52.210926Z","title":"Per- gaussian embedding-based deformation for deformable 3d gaussian splatting,","venue":null,"work_id":"36edf8fb-eb70-4868-a6d9-1a32e4fbe694","year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.554891Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:7d605cdf68304ff895ea0a134cfa02497f246fe24ff3104c6fc41ca6fb5de234","observation_id":"661a4fdc-7fa6-478b-95d8-720ec08fe27e","resolution":{"observed_at":"2026-08-06T21:56:52.237623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:52.090490Z","title":"Strang and T","venue":null,"work_id":"e8d8af5f-2711-4417-a96a-644e43670469","year":null},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.639422Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:b9d84255c45c7f7207c1811a33fc917e1b97648a6252ce48c1c1d53831662200","observation_id":"bbb15e6e-1cb6-49f4-9f42-121f64d3d66a","resolution":{"observed_at":"2026-08-06T21:56:52.139143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00027","last_updated":"2024-03-13T18:51:29Z","snapshot_observed_at":"2026-07-06T17:09:59.848387Z","submitted_at":"2023-12-29T02:59:40Z","title":"Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.00027","snapshot_observed_at":"2026-08-06T21:56:48.706931Z","title":"Efficient multi-scale network with learnable discrete wavelet transform for blind motion deblurring,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.706931Z"},"links":{"cited_paper":"/paper/2401.00027","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:48d08228292bd2c89f86845e6b15e119b0ce0ec2137201c579820749fdeafb0c","observation_id":"9eac09ce-b3eb-41b1-b836-f09a99db2179","resolution":{"observed_at":"2026-08-06T21:56:48.706931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.06638","last_updated":"2021-10-20T07:24:13Z","snapshot_observed_at":"2026-07-06T11:47:33.842743Z","submitted_at":"2021-09-13T08:02:38Z","title":"Learnable Discrete Wavelet Pooling (LDW-Pooling) For Convolutional Networks","version":4},"cited_work":{"arxiv_id":"2109.06638","doi":null,"metadata_source":"pith","pith_arxiv_id":"2109.06638","snapshot_observed_at":"2026-08-06T21:56:50.514882Z","title":"Learnable Discrete Wavelet Pooling (LDW-Pooling) For Convolutional Networks","venue":"cs.CV","work_id":"600eb20a-ae3e-485a-8a46-2c65420bab76","year":2021},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.778601Z"},"links":{"cited_paper":"/paper/2109.06638","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:637272bd32f289010a195163e238d8b02f6f4954a4ea074d8e2663dc02737418","observation_id":"3ce827d4-ba54-4908-8a9e-db7b5bd95573","resolution":{"observed_at":"2026-08-06T21:56:50.585388Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:52.022308Z","title":"A novel learn- able orthogonal wavelet unit neural network with perfection reconstruction constraint relaxation for image classification,","venue":null,"work_id":"81dd3937-4905-406a-a8c1-7b0546844a79","year":2023},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.814765Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:70e68a3aaf40b2bc748458a551eab4f9f71a21b865656e25367f406c9ee4411c","observation_id":"2a4eb9d9-57fa-4153-be6a-8ea3eb7dc388","resolution":{"observed_at":"2026-08-06T21:56:52.050360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:51.924030Z","title":"A lattice- structure-based trainable orthogonal wavelet unit for image classification,","venue":null,"work_id":"f44ee5eb-c79a-4020-a15d-f5b0f28f458f","year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.892330Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:94d15a2b266b4c56277b2fd7f38e0f1b8389f3d5ac651cb7c1de0144c2b49167","observation_id":"77de5e9c-9a07-4bd8-8269-a99a948c7eef","resolution":{"observed_at":"2026-08-06T21:56:51.970645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:51.846295Z","title":"Biorthogonal lattice tunable wavelet units and their imple- mentation in convolutional neural networks for computer vi- sion problems,","venue":null,"work_id":"cd57defb-8cc2-4b7d-89fb-bda5ded3b28f","year":2025},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.981443Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:edb94f48502d7b69ecda61403bbc560033b0b9d02d2055ca7819eecd4316e146","observation_id":"2dec9723-b4e6-4fa7-ae4b-b55ffd51e670","resolution":{"observed_at":"2026-08-06T21:56:51.877656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:51.762463Z","title":"Masked wavelet representation for compact neural radiance fields,","venue":null,"work_id":"53331761-9b03-4bae-b84d-8b303ca62a8a","year":2023},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.029079Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:a4aec7a6cf11be12421da005e1422346149f8e63794cc779f45f53c1f30e9b02","observation_id":"7cb40dd9-f1a3-433f-b6fb-4fef389d1863","resolution":{"observed_at":"2026-08-06T21:56:51.805690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.04826","last_updated":"2023-10-26T07:05:19Z","snapshot_observed_at":"2026-07-06T16:04:18.544245Z","submitted_at":"2023-08-09T09:24:56Z","title":"WaveNeRF: Wavelet-based Generalizable Neural Radiance Fields","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.04826","snapshot_observed_at":"2026-08-06T21:56:49.079958Z","title":"Wavenerf: Wavelet-based generalizable neural radiance fields,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.079958Z"},"links":{"cited_paper":"/paper/2308.04826","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:93d8bcad7c9c8292a783411d3eeb21fbdabe59365cc3c435449262d2c4392f20","observation_id":"722806cb-721f-47dd-a69c-8ce6c904d5af","resolution":{"observed_at":"2026-08-06T21:56:49.079958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.06191","last_updated":"2024-07-17T21:14:34Z","snapshot_observed_at":"2026-07-06T17:14:28.456108Z","submitted_at":"2024-01-11T11:50:36Z","title":"TriNeRFLet: A Wavelet Based Triplane NeRF Representation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.06191","snapshot_observed_at":"2026-08-06T21:56:49.124361Z","title":"Trinerflet: A wavelet based triplane nerf representation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.124361Z"},"links":{"cited_paper":"/paper/2401.06191","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:a8d6778cf573bd6c47db825e9083dc9cd518acb65ce49f16c3a60706fd5c5aff","observation_id":"9c3e7633-d55d-495c-a93e-b488db50018b","resolution":{"observed_at":"2026-08-06T21:56:49.124361Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:51.658494Z","title":"Dwtnerf: Boosting few-shot neural radiance fields via discrete wavelet transform,","venue":null,"work_id":"4de27375-a3be-41f5-8ffc-3a5cb642fd4b","year":null},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.216774Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:ed4e543657a122a85ba32ef9b9a4ba571059f1c4b40727cf1c15652ebd7803a8","observation_id":"12bec0ce-713a-4375-bd21-9db30bf12fd4","resolution":{"observed_at":"2026-08-06T21:56:51.692793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:49.341775Z","title":"Instant neural graphics primitives with a multiresolution hash encoding,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.341775Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:96286d5b4797bd5d4b1a67dddbc1fff19b6c9ca81380e43ba4723f099a55ea49","observation_id":"e9576100-0cf3-4693-94e9-daf31978cc0d","resolution":{"observed_at":"2026-08-06T21:56:49.341775Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.14231","last_updated":"2025-01-24T04:37:57Z","snapshot_observed_at":"2026-08-05T16:17:37.603706Z","submitted_at":"2025-01-24T04:37:57Z","title":"Micro-macro Wavelet-based Gaussian Splatting for 3D Reconstruction from Unconstrained Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.14231","snapshot_observed_at":"2026-08-06T21:56:49.387468Z","title":"Micro-macro wavelet-based gaussian splatting for 3d reconstruction from unconstrained images,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.387468Z"},"links":{"cited_paper":"/paper/2501.14231","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:d3464d95fa1e860c97f2afb248f192632766346a79b31efce00614d2b80b8682","observation_id":"99a5b5f3-f2dc-4804-9d6e-7af0acad8af7","resolution":{"observed_at":"2026-08-06T21:56:49.387468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:49.438971Z","title":"Taming 3dgs: High-quality radiance fields with limited resources,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.438971Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:42f7822f45c3363913d2daaadecf367b575392c8e26c607ff045f4ab2bf25f4f","observation_id":"e2b7cfaf-cbe0-429f-9173-15e585cd0405","resolution":{"observed_at":"2026-08-06T21:56:49.438971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:49.531405Z","title":"Reducing the memory footprint of 3d gaussian splatting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.531405Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:5ff66bf0de92d77aad16882b182ac62052d428e3a98584a5e571bb728d1510d9","observation_id":"917a894e-df07-4ca9-9e83-a422f6063299","resolution":{"observed_at":"2026-08-06T21:56:49.531405Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14166","last_updated":"2024-10-16T07:07:21Z","snapshot_observed_at":"2026-08-06T04:45:32.609220Z","submitted_at":"2024-03-21T06:34:46Z","title":"Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14166","snapshot_observed_at":"2026-08-06T21:56:49.584381Z","title":"Mini-splatting: Representing scenes with a constrained number of gaussians,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.584381Z"},"links":{"cited_paper":"/paper/2403.14166","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:89927713e317c5b18243d06628917b9964eb41c5ff1d81aa284c540e09f683fb","observation_id":"7b5d6d08-8cf4-4c89-b508-ee050c502caf","resolution":{"observed_at":"2026-08-06T21:56:49.584381Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:51.591522Z","title":"Compact 3d gaussian representation for radiance field,","venue":null,"work_id":"36336a0e-18fb-4e94-9ae2-c3acfd13f1a2","year":2024},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.653558Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:7479b3901722679ab5c3d3ddd9a1f8273fe503d5a4dbdfd1859c0ec3fe8d3d11","observation_id":"6096485d-4c47-4c1c-acf8-13ac701defcb","resolution":{"observed_at":"2026-08-06T21:56:51.624032Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:51.495764Z","title":"Image quality assessment: from error visibility to structural similar- ity,","venue":null,"work_id":"cfe1494a-a80e-474e-b460-f978e05a1297","year":2004},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.713168Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:d975b6fb169f29858873f570e6f4c2b7a3ccbd8dca34bbe99b88c3be4d61985f","observation_id":"abe8c7cb-a394-43b4-999c-2979a67e3075","resolution":{"observed_at":"2026-08-06T21:56:51.541580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:51.425793Z","title":"Local light field fusion: Practical view synthesis with prescriptive sampling guidelines,","venue":null,"work_id":"cb217fa8-ad50-42ef-8319-503fea7ae1d2","year":2019},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.755258Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:5cc830017b347803480812fc73d7ee6eae2b438d8ef4cbb7e088e5ed7e615085","observation_id":"51448f8f-0098-4e0e-93d1-d60eef562f04","resolution":{"observed_at":"2026-08-06T21:56:51.468432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:51.303672Z","title":"Wavecnet: Wavelet integrated cnns to suppress aliasing effect for noise-robust im- age classification,","venue":null,"work_id":"c65a20b4-1526-4efe-8bd7-a39864519656","year":2021},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.796665Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:0a61eeb82315abf95332f0da0f37506c5b24bc5d8ea216849dfb90782132cebc","observation_id":"08c13ebe-8309-4fa7-9a81-a2cb0a2acdee","resolution":{"observed_at":"2026-08-06T21:56:51.339741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:51.173209Z","title":"Mip-nerf 360: Unbounded anti-aliased neural radiance fields,","venue":null,"work_id":"36e7793e-7331-4fab-a5c2-498b2759e7c5","year":2022},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.834717Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:740a2d26f3534b026321d859e03f9a21e0658ac2094e8fb7a44916db00a226a8","observation_id":"9307ed19-f114-4147-87de-e59147b4fd97","resolution":{"observed_at":"2026-08-06T21:56:51.223923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:51.091493Z","title":"The unreasonable effectiveness of deep features as a percep- tual metric,","venue":null,"work_id":"b71a049c-d940-4678-a896-f647e612cf7c","year":2018},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.906022Z"},"links":{"citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:6643b1177dab232f980a3480899f55a51ed216a6a191405329ab21b19f918eec","observation_id":"14b0b3f5-0664-44d1-9170-199ea7b936e9","resolution":{"observed_at":"2026-08-06T21:56:51.125749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.00773","last_updated":"2025-04-01T13:23:34Z","snapshot_observed_at":"2026-08-07T16:17:29.614697Z","submitted_at":"2025-04-01T13:23:34Z","title":"DropGaussian: Structural Regularization for Sparse-view Gaussian Splatting","version":1},"cited_work":{"arxiv_id":"2504.00773","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.00773","snapshot_observed_at":"2026-08-06T21:56:50.146872Z","title":"DropGaussian: Structural Regularization for Sparse-view Gaussian Splatting","venue":"cs.CV","work_id":"008e3516-5b8c-47f0-8a9f-01495faaaadb","year":2025},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.943759Z"},"links":{"cited_paper":"/paper/2504.00773","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:c95a81565538181254b73eca7a141fa0fc1b502ae16d713d9360fd81aadd622f","observation_id":"707ad52a-b3e9-4491-b1a9-fb3ce2e644f0","resolution":{"observed_at":"2026-08-06T21:56:50.236252Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.04958","last_updated":"2020-03-23T17:21:07Z","snapshot_observed_at":"2026-07-06T08:43:31.085150Z","submitted_at":"2019-12-03T11:44:01Z","title":"Analyzing and Improving the Image Quality of StyleGAN","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.04958","snapshot_observed_at":"2026-08-06T21:56:50.035366Z","title":"Analyzing and improving the image quality of stylegan,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:50.035366Z"},"links":{"cited_paper":"/paper/1912.04958","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:638762f3481147abbaeb8a1ecd6ea211798821212fb28b544ef4a29bd3dcd326","observation_id":"86f3ad13-ea14-40d0-9f50-a45b76a4970c","resolution":{"observed_at":"2026-08-06T21:56:50.035366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.20323","last_updated":"2024-05-30T17:57:08Z","snapshot_observed_at":"2026-07-06T18:22:53.072592Z","submitted_at":"2024-05-30T17:57:08Z","title":"$\\textit{S}^3$Gaussian: Self-Supervised Street Gaussians for Autonomous Driving","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.20323","snapshot_observed_at":"2026-08-06T21:56:48.112386Z","title":"Available: https://arxiv.org/abs/2405.20323 2","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:48.112386Z"},"links":{"cited_paper":"/paper/2405.20323","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:fbf3894485abe73abaed0047fd36b630eea3677432b0f15b8e75356fb613126a","observation_id":"85a3b91c-ce9a-4d98-977d-e1db3446ba86","resolution":{"observed_at":"2026-08-06T21:56:48.112386Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12637","last_updated":"2025-08-09T09:39:15Z","snapshot_observed_at":"2026-08-05T14:41:29.001238Z","submitted_at":"2025-01-22T04:53:12Z","title":"DWTNeRF: Boosting Few-shot Neural Radiance Fields via Discrete Wavelet Transform","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12637","snapshot_observed_at":"2026-08-06T21:56:49.286256Z","title":"Available: https://arxiv.org/abs/2501.12637 2 9","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:49.286256Z"},"links":{"cited_paper":"/paper/2501.12637","citing_paper":"/paper/2506.23042"},"observation_digest":"sha256:7df123b21bfa2f567516a1ce32f78ef8a36b9cf47ab6f18385c18c391a85fecd","observation_id":"e72d7f59-69c4-4df2-acce-b8be3d8ab627","resolution":{"observed_at":"2026-08-06T21:56:49.286256Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.23042","last_updated":"2025-06-29T00:27:17Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T21:49:26.877462Z","submitted_at":"2025-06-29T00:27:17Z","title":"From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":4,"verified_fuzzy":22},"total_outbound_references":43},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2506.23042."}